{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.functional as F\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets,transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "epochs = 10\n",
    "batch_size = 200\n",
    "learning_rate = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_loader = torch.utils.data.DataLoader(\n",
    "    datasets.MNIST('../data',train=True,\n",
    "                    transform=transforms.Compose([transforms.ToTensor(),\n",
    "                    transforms.Normalize((0.1307,),(0.3081,))])),\n",
    "    batch_size = batch_size,shuffle = True)\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "    datasets.MNIST('../data',train=False,\n",
    "                    transform=transforms.Compose([transforms.ToTensor(),\n",
    "                    transforms.Normalize((0.1307,),(0.3081,))])),\n",
    "    batch_size = batch_size,shuffle = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "torch.Size([200, 1, 28, 28])\n",
      "tensor([ 9,  3,  3,  0,  9,  6,  9,  8,  1,  4,  0,  7,  3,  2,\n",
      "         9,  2,  9,  6,  9,  3,  1,  4,  4,  7,  3,  0,  2,  4,\n",
      "         9,  8,  2,  7,  9,  4,  0,  9,  6,  2,  3,  4,  8,  4,\n",
      "         6,  7,  6,  5,  2,  7,  7,  7,  0,  0,  1,  8,  9,  0,\n",
      "         0,  5,  3,  9,  1,  0,  9,  0,  9,  4,  4,  0,  4,  3,\n",
      "         0,  3,  1,  5,  0,  6,  1,  5,  7,  1,  8,  6,  6,  4,\n",
      "         0,  2,  2,  3,  3,  4,  0,  6,  6,  4,  4,  2,  6,  1,\n",
      "         6,  4,  1,  7,  3,  8,  9,  2,  5,  7,  0,  7,  3,  2,\n",
      "         1,  6,  6,  5,  5,  6,  4,  4,  8,  9,  2,  7,  2,  8,\n",
      "         9,  8,  3,  8,  4,  2,  6,  2,  3,  8,  7,  7,  1,  7,\n",
      "         0,  4,  2,  2,  9,  8,  9,  2,  6,  9,  2,  6,  3,  7,\n",
      "         0,  1,  4,  5,  4,  9,  3,  5,  2,  1,  7,  5,  2,  7,\n",
      "         5,  4,  8,  6,  7,  0,  5,  0,  5,  6,  6,  0,  7,  4,\n",
      "         4,  5,  4,  4,  5,  1,  9,  6,  4,  7,  1,  3,  2,  6,\n",
      "         2,  3,  3,  6])\n"
     ]
    }
   ],
   "source": [
    "for idx,(train,target) in enumerate(train_loader):\n",
    "    print(idx)\n",
    "    print(train.shape)\n",
    "    print(target)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class MLP(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(MLP,self).__init__()\n",
    "        self.model = nn.Sequential(\n",
    "            nn.Linear(784,200),\n",
    "            nn.ReLU(inplace = True),\n",
    "            nn.Linear(200,200),\n",
    "            nn.ReLU(inplace = True),\n",
    "            nn.Linear(200,10),\n",
    "            nn.ReLU(inplace=True)\n",
    "        )\n",
    "    \n",
    "    def forward(self,x):\n",
    "        x = self.model(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([  8.0283,   2.9947,   7.2803,  11.3934,  10.1908,   9.0027,\n",
      "          4.9886,   0.0000,  11.1896,   6.1969,   8.8827,   6.0346,\n",
      "          1.6992,   8.6076,   0.0000,   9.2996,   7.4054,   6.5572,\n",
      "          0.0000,   4.4171,   6.8741,   9.8785,   6.2014,   6.9179,\n",
      "          0.0000,   0.7736,   5.2885,   5.1085,   7.3494,   7.9640,\n",
      "          3.4683,   9.8528,  11.9251,   6.5754,   0.7580,   6.3825,\n",
      "          9.6941,   0.0000,   3.5718,   0.0000,   6.1257,   2.8271,\n",
      "          4.0614,   0.0000,  12.2049,   6.1494,   9.8572,   7.9559,\n",
      "          0.0000,   7.1491,   7.4322,   7.1322,   6.7709,   5.0640,\n",
      "          1.0072,   4.7917,   5.2630,   5.9734,  13.6068,   9.3586,\n",
      "         13.1861,   9.0020,   8.8005,   6.0497,   9.5449,  10.5247,\n",
      "          7.2888,   6.8119,   7.1597,   6.1879,   8.5015,   0.0000,\n",
      "          4.2134,   0.0000,   8.5744,   5.1043,   0.0000,   5.7469,\n",
      "          9.3757,   9.9559,   4.0903,   0.0000,   7.1782,   9.5431,\n",
      "          8.1841,   0.0000,   8.5303,   0.0000,   5.6320,   7.0026,\n",
      "          7.1977,   5.2990,   0.0000,   7.3109,   2.9831,   9.3561,\n",
      "          1.7354,   7.7418,   8.5044,   0.0000,   7.0951,   0.0000,\n",
      "          0.0000,   8.5855,  10.8149,   7.5610,   0.0000,   0.0000,\n",
      "          0.6951,   8.1282,   8.0520,  10.0476,   3.6824,   3.7951,\n",
      "          9.9606,   3.4756,   7.7043,   9.1181,   8.9382,   9.2413,\n",
      "          0.0000,   6.9332,   8.8672,   2.0468,   3.3980,   7.8888,\n",
      "         10.7897,   9.0591,   0.2808,   0.0000,   7.5147,   6.5976,\n",
      "          7.9569,   9.2739,   0.1982,   6.0635,   7.5758,  13.8444,\n",
      "          6.7421,   6.7009,   3.3706,  10.7301,  12.3280,   9.3641,\n",
      "          7.3332,   8.6157,   2.3594,   8.4774,   8.0855,   4.7574,\n",
      "          6.9763,  10.9986,   9.1672,   7.7835,   0.7638,   4.6728,\n",
      "          7.5527,   2.8276,   9.6233,   3.9724,   9.3706,   6.7485,\n",
      "          0.0000,   0.5895,   8.3487,  12.9576,   8.9927,   6.7268,\n",
      "         10.0189,  10.5672,   8.4287,   8.3514,   9.1541,   0.0000,\n",
      "         10.5096,   7.5124,   9.4662,  11.9718,  10.6964,   0.0000,\n",
      "          4.2263,   0.0000,   0.0000,   0.0000,   1.7156,  10.9421,\n",
      "          8.1775,   7.2963,   6.3294,   8.9562,   0.0000,   8.9083,\n",
      "          0.9344,  12.0424,   8.8250,   5.8788,  10.4602,   6.4877,\n",
      "          9.4245,   8.9908])\n",
      "tensor([ 3,  3,  2,  5,  9,  1,  3,  0,  6,  0,  5,  2,  9,  0,\n",
      "         0,  1,  6,  1,  0,  3,  3,  1,  2,  9,  0,  9,  9,  5,\n",
      "         9,  6,  9,  5,  6,  0,  2,  9,  2,  0,  5,  0,  6,  2,\n",
      "         0,  0,  6,  9,  1,  3,  0,  5,  6,  6,  9,  4,  5,  4,\n",
      "         5,  9,  2,  4,  2,  1,  0,  3,  1,  3,  9,  4,  9,  0,\n",
      "         5,  0,  3,  0,  2,  2,  0,  1,  5,  2,  3,  0,  9,  2,\n",
      "         6,  0,  3,  0,  9,  4,  3,  9,  0,  4,  1,  1,  2,  1,\n",
      "         9,  0,  9,  0,  0,  9,  4,  3,  0,  0,  3,  2,  9,  3,\n",
      "         9,  3,  0,  5,  6,  2,  9,  1,  0,  6,  9,  5,  1,  2,\n",
      "         5,  3,  9,  0,  3,  5,  4,  5,  2,  0,  4,  4,  0,  9,\n",
      "         9,  3,  5,  2,  1,  0,  2,  5,  4,  2,  3,  6,  4,  6,\n",
      "         6,  2,  9,  2,  6,  6,  1,  2,  0,  9,  2,  3,  3,  5,\n",
      "         0,  4,  4,  9,  9,  0,  9,  1,  2,  4,  4,  0,  4,  0,\n",
      "         0,  0,  2,  6,  5,  3,  2,  3,  0,  1,  3,  6,  1,  0,\n",
      "         0,  9,  3,  1])\n",
      "tensor([  6.7849,   8.4687,   7.8268,   8.8687,   8.1079,  10.1897,\n",
      "          9.9568,   7.2944,   0.0000,   8.2836,   8.5795,   8.4586,\n",
      "         13.9198,   8.6225,   9.2007,   0.0000,  11.5373,   8.1903,\n",
      "          3.6322,   2.1793,   7.9698,  11.3235,   4.0796,   6.4896,\n",
      "          4.4079,  11.1284,   0.0000,   9.9035,   8.0710,   7.4700,\n",
      "          8.3589,   9.8484,  13.0697,  12.3606,  10.0981,   0.0000,\n",
      "         10.5970,   8.7816,   4.2890,   7.3345,   5.5898,   7.6523,\n",
      "          9.3397,   3.9289,   9.9620,   8.4476,  10.4440,   0.0000,\n",
      "          6.2642,   7.7594,   9.1304,   0.0000,  11.3532,   9.2989,\n",
      "          0.0000,   9.6299,   1.3674,   0.0000,   7.1987,  12.2448,\n",
      "          0.0000,   6.0735,   7.6802,   9.6032,  10.2844,   0.7732,\n",
      "          7.3379,   8.6859,   4.9715,   9.8837,   0.0000,   6.1361,\n",
      "          0.0000,   7.8166,   6.1113,   7.7591,   0.0000,   9.8937,\n",
      "          7.3094,   9.1937,   5.2828,   9.1123,   7.4856,   8.3207,\n",
      "          8.7717,   5.1502,   0.0000,   0.0000,   2.8432,   3.4460,\n",
      "          6.6913,   6.9714,   4.9885,   4.7550,   0.0000,   8.4763,\n",
      "          8.8114,   9.8116,   6.2497,   8.8500,   8.6559,   3.5923,\n",
      "          9.7306,   9.0414,   0.0000,   6.7793,   8.5697,   0.0000,\n",
      "          8.9002,   5.6852,   7.3871,   9.7038,   0.0000,   4.6104,\n",
      "          0.9051,   0.0000,   5.8379,   5.0650,   8.3928,   0.0000,\n",
      "          6.3886,   7.5053,   3.2618,   5.0606,   0.0000,   5.6439,\n",
      "          8.6662,   0.0000,   7.0805,   9.1889,   0.0000,   8.3557,\n",
      "          0.0000,   5.5617,   2.8737,   6.3464,  10.1595,   9.3673,\n",
      "          0.0000,   8.5827,   5.7517,   0.0000,   8.7741,   0.0000,\n",
      "         10.4952,   7.1829,   7.8346,   1.3122,   9.9416,   5.5187,\n",
      "          0.2107,   8.9455,   8.6839,   7.8550,   8.2361,   4.6842,\n",
      "          5.7113,   1.6425,   1.6639,  15.6275,   7.9782,   9.6107,\n",
      "         15.1427,   0.0000,   5.2338,   8.7651,   6.8954,   7.2906,\n",
      "          0.0000,  10.2103,   8.2896,   0.0000,   0.9003,   0.0000,\n",
      "          7.0362,   0.0000,   0.0000,   7.1807,   5.5102,  10.1571,\n",
      "          0.0000,   7.9620,   8.4816,   9.8434,   1.0118,   4.3969,\n",
      "          9.4033,   4.3994,   0.0000,   0.0943,   4.1684,   6.3635,\n",
      "          7.6509,   8.7815,   6.8771,  10.9066,   8.6725,   9.6424,\n",
      "         10.7559,   6.5045])\n",
      "tensor([ 3,  5,  1,  2,  1,  6,  2,  3,  0,  6,  9,  2,  0,  1,\n",
      "         1,  0,  6,  5,  6,  2,  2,  6,  2,  2,  9,  5,  0,  6,\n",
      "         4,  0,  4,  5,  2,  4,  0,  0,  6,  1,  1,  1,  2,  0,\n",
      "         1,  2,  5,  1,  4,  0,  6,  5,  1,  0,  0,  3,  0,  2,\n",
      "         4,  0,  2,  6,  0,  0,  2,  9,  3,  9,  9,  9,  0,  6,\n",
      "         0,  5,  0,  2,  0,  5,  0,  2,  3,  3,  0,  1,  6,  1,\n",
      "         5,  0,  0,  0,  9,  0,  9,  9,  2,  4,  0,  9,  1,  4,\n",
      "         3,  0,  5,  0,  1,  1,  0,  5,  1,  0,  0,  0,  0,  4,\n",
      "         0,  9,  9,  0,  3,  9,  6,  0,  9,  9,  4,  5,  0,  4,\n",
      "         0,  0,  3,  1,  0,  9,  0,  0,  0,  2,  4,  5,  0,  6,\n",
      "         0,  0,  1,  0,  4,  5,  4,  3,  1,  3,  3,  9,  5,  9,\n",
      "         2,  5,  5,  2,  6,  2,  4,  9,  2,  0,  6,  3,  0,  0,\n",
      "         0,  1,  9,  0,  6,  0,  9,  0,  0,  0,  9,  6,  0,  1,\n",
      "         1,  4,  2,  9,  0,  1,  0,  9,  9,  0,  4,  2,  6,  6,\n",
      "         6,  4,  2,  3])\n",
      "tensor([  4.7898,   3.6021,   5.0871,   8.1177,  10.3423,   9.1633,\n",
      "          7.6118,   5.6836,  10.5512,   4.4585,   7.9494,   9.8639,\n",
      "         11.2469,   7.9231,   8.3113,   9.6259,   7.9607,   7.5897,\n",
      "          3.1314,  13.0888,   7.9153,   7.5930,   5.4195,   8.6222,\n",
      "          3.6283,   5.6059,   0.0000,  13.1202,   0.0000,   4.6157,\n",
      "          7.7367,   8.6833,   7.2641,   6.1647,  10.2482,   8.8703,\n",
      "          5.8165,   7.7840,   4.5833,  11.1010,   6.2317,   0.2380,\n",
      "         10.7026,   8.5700,   5.3611,   6.8095,   9.7589,   0.0000,\n",
      "          9.1181,   9.6859,   6.1540,   0.0602,   5.0935,   6.6412,\n",
      "          5.7533,   9.4119,   5.3496,   8.4183,   0.0000,  11.0137,\n",
      "         10.5694,  13.3150,   7.8459,   7.6004,   0.3819,  10.1239,\n",
      "          8.4874,   7.6954,   5.5816,   9.5683,   9.0651,   0.0000,\n",
      "         12.8836,   8.9667,   9.9180,   5.4755,   7.4557,   0.0000,\n",
      "          8.0395,   6.8342,   0.0000,   9.2148,   9.0220,  11.1217,\n",
      "          9.1776,   7.9421,   6.8003,   8.9645,  10.2165,   6.6405,\n",
      "         10.7501,   8.6690,   8.9377,   6.7407,   0.2152,   9.5607,\n",
      "          0.0000,   7.5464,   5.7984,   8.2194,   5.8731,   8.7773,\n",
      "          9.9517,   7.8952,   3.3152,   9.1858,   7.8925,   8.2386,\n",
      "          9.2160,   6.8613,   8.2660,   7.5771,   6.4846,  12.0377,\n",
      "          0.0000,   8.1144,   6.4598,   0.5550,   0.0000,   7.6741,\n",
      "          9.1673,   5.3441,   0.0000,   4.7642,   0.0000,   5.5093,\n",
      "          9.3342,  10.8823,   0.7381,   6.7888,   9.1389,   9.4152,\n",
      "          9.8396,   7.1239,  13.5800,   6.3969,   0.0000,   7.3425,\n",
      "         10.3365,  10.7849,   5.0946,   1.3677,   0.0000,   6.4905,\n",
      "          7.7401,   1.2924,   8.6329,  12.4299,   7.9110,   7.6018,\n",
      "         12.3787,  11.0439,   5.6234,   9.1225,   6.2676,   9.8282,\n",
      "          4.2890,   8.9050,   0.0000,   5.3418,   6.5155,  10.0348,\n",
      "          8.1302,   9.8466,   7.4323,   5.4187,   8.5197,   8.9420,\n",
      "         11.4292,   3.2743,   0.0000,   0.0000,   9.8823,   7.3120,\n",
      "          9.9680,   8.4553,   6.8978,   9.2825,   6.6686,   6.9195,\n",
      "          8.7370,   8.7121,  10.6892,  10.4517,   6.6892,   9.6608,\n",
      "         11.6783,   7.2604,   7.3115,   7.0947,   9.6444,   7.0092,\n",
      "         12.5672,   6.2450,   4.9678,   9.0089,   8.6191,   3.8944,\n",
      "          0.0000,   8.0459])\n",
      "tensor([ 1,  2,  0,  6,  2,  3,  9,  2,  9,  9,  5,  5,  0,  5,\n",
      "         9,  6,  5,  3,  9,  6,  4,  3,  3,  4,  3,  6,  0,  5,\n",
      "         0,  3,  0,  9,  2,  9,  6,  5,  4,  1,  3,  0,  1,  3,\n",
      "         6,  6,  6,  0,  1,  0,  6,  6,  3,  3,  3,  0,  5,  9,\n",
      "         1,  4,  0,  6,  0,  2,  5,  5,  3,  1,  1,  6,  5,  1,\n",
      "         2,  0,  6,  3,  9,  4,  4,  0,  6,  0,  0,  3,  2,  4,\n",
      "         5,  0,  5,  1,  6,  2,  6,  9,  1,  0,  1,  1,  0,  2,\n",
      "         2,  3,  9,  6,  1,  1,  2,  1,  1,  4,  1,  9,  6,  9,\n",
      "         2,  6,  0,  5,  5,  9,  0,  0,  1,  0,  0,  5,  0,  1,\n",
      "         2,  0,  9,  9,  1,  6,  2,  4,  3,  1,  0,  9,  6,  0,\n",
      "         9,  3,  0,  0,  1,  9,  2,  0,  0,  1,  4,  5,  0,  2,\n",
      "         3,  9,  0,  4,  0,  3,  3,  4,  9,  4,  1,  9,  9,  3,\n",
      "         4,  3,  0,  0,  1,  6,  9,  1,  5,  3,  9,  2,  3,  4,\n",
      "         6,  9,  1,  1,  3,  4,  0,  2,  1,  5,  4,  6,  4,  3,\n",
      "         1,  6,  0,  2])\n",
      "tensor([  1.2297,   8.4226,  10.3148,   0.0000,  11.5105,  11.0626,\n",
      "          5.7220,   0.0000,   9.3278,   1.3929,   9.0825,   9.0793,\n",
      "         10.8601,   5.0894,  15.0863,   0.7112,   5.8621,   0.0000,\n",
      "          5.3319,   9.4426,   6.6041,   9.4686,  10.7904,   9.8561,\n",
      "          9.0623,   9.0535,   5.5919,   1.2428,   0.3105,   9.7071,\n",
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      "         13.2171,   3.1692,   0.0000,   3.2230,   0.6699,   7.0746,\n",
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      "          7.8497,   6.0420,   2.5105,  11.4113,   0.0000,   9.7312,\n",
      "         12.5771,   0.0000])\n",
      "tensor([ 3,  9,  5,  0,  6,  3,  3,  0,  6,  9,  3,  3,  4,  5,\n",
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      "         0,  1,  2,  2,  5,  3,  0,  2,  1,  0,  6,  2,  9,  4,\n",
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      "         9,  3,  4,  0,  6,  0,  1,  2,  2,  0,  5,  4,  0,  9,\n",
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      "         9,  3,  3,  9,  1,  0,  3,  5,  2,  3,  0,  1,  0,  3,\n",
      "         9,  4,  9,  2,  3,  3,  9,  3,  3,  0,  6,  1,  3,  0,\n",
      "         3,  9,  0,  0,  3,  0,  1,  0,  1,  1,  1,  1,  3,  0,\n",
      "         0,  1,  3,  0])\n",
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      "          8.4340,   5.9323,   0.0000,  10.0837,   3.8233,  11.3228,\n",
      "          3.0107,   9.7560,   5.6637,   7.2436,  10.5973,   5.1096,\n",
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      "         10.0414,  12.9185,   9.0940,  12.4841,   4.2452,   1.7726,\n",
      "         10.5787,  11.3113,  10.8494,  10.4854,   8.4021,   9.8533,\n",
      "          0.0000,  10.2196,  11.4146,   7.1723,   6.2795,  10.1417,\n",
      "          3.0795,   6.0506,   9.3156,   7.8645,   9.7401,  10.4437,\n",
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      "          8.6251,   0.0000,   8.2945,   6.0920,   9.9117,   9.7024,\n",
      "          7.5231,   6.7619,   4.9098,   1.0510,  10.5675,   0.2516,\n",
      "         12.0922,   8.0892,   6.2849,   8.8892,   7.2028,   8.8951,\n",
      "          4.9535,   9.7472,   7.4927,   8.8992,   0.0000,   5.8389,\n",
      "          5.7025,   6.8127,   5.9229,   7.2170,   9.1381,   0.0000,\n",
      "          6.4816,   6.9196,   6.6046,   9.6016,   8.4152,   3.5723,\n",
      "          3.5860,   0.0000,   9.2808,   6.1523,   9.7138,   7.2694,\n",
      "         11.9209,   0.0000,   7.6754,   8.8366,  14.5183,   9.6638,\n",
      "          0.9079,   0.0000,   8.5899,   0.0000,   3.7102,  11.3619,\n",
      "          9.8886,   5.0682,   9.4852,   5.1419,   4.7047,   8.4803,\n",
      "          7.0711,   2.0965,   4.4581,   1.1443,   7.6773,   8.6556,\n",
      "         10.9512,   9.9509,   6.7551,   9.5169,  10.0494,   9.9427,\n",
      "          4.4319,   8.8194,   8.7145,  11.9214,   5.2110,   9.6069,\n",
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      "          7.7708,   8.4025,   0.0000,   0.0000,   9.9927,   4.4974,\n",
      "          5.8709,   0.0000,   7.0488,   5.5443,   9.1528,   6.9968,\n",
      "          6.9797,   8.5465,   5.8192,   5.3413,   6.0714,   1.5300,\n",
      "         10.8152,   9.9606,   6.8614,   6.9023,   7.3533,   7.6837,\n",
      "         11.0324,   9.0058,   7.0570,   5.6858,   8.4283,   4.7524,\n",
      "          9.5684,   0.0000,   9.9076,   7.4994,   9.8591,  10.9891,\n",
      "          0.1108,   7.9407,  11.6435,   1.3295,   6.3440,   7.9096,\n",
      "          9.4501,   0.0000])\n",
      "tensor([ 0,  6,  2,  6,  2,  3,  3,  9,  6,  2,  6,  0,  6,  9,\n",
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      "         6,  3,  9,  6,  2,  1,  3,  3,  0,  0,  1,  2,  1,  4,\n",
      "         4,  9,  3,  1,  0,  1,  0,  9,  5,  0,  2,  0,  9,  2,\n",
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      "         1,  4,  1,  5,  9,  6,  3,  4,  2,  1,  6,  9,  1,  0,\n",
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      "         5,  5,  0,  2,  9,  0,  6,  0,  1,  6,  5,  4,  5,  4,\n",
      "         3,  6,  1,  0])\n",
      "tensor([  3.2204,   2.4747,   9.8201,   0.0000,   9.6168,   9.1663,\n",
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      "          6.9499,   1.8485,   0.0000,   4.5057,   5.3526,   4.2827,\n",
      "          6.6058,   2.0864])\n",
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      "         1,  5,  9,  0])\n",
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      "          0.0000,   9.0446,  12.1005,   7.2142,  10.9385,   9.5797,\n",
      "          9.8341,   9.6099])\n",
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      "         6,  4,  1,  1])\n",
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      "         10.1583,  10.5409])\n",
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      "         3,  4,  4,  0])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "         0,  5,  4,  6])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "          8.4274,   2.3077,   5.0665,   5.3766,   8.4575,   0.0000,\n",
      "          7.3326,   8.8848])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "          5.2450,   9.4610])\n",
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      "         3,  3,  0,  4])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "         10.2761,   6.3072,  11.0114,   7.1422,   9.3112,   7.9137,\n",
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      "         9,  0,  2,  5])\n",
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      "         9,  1,  0,  9])\n",
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      "         4,  5,  0,  4])\n",
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      "          9.4958,   8.5685])\n",
      "tensor([ 9,  1,  0,  3,  3,  5,  2,  0,  5,  9,  4,  0,  9,  5,\n",
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      "         3,  4,  1,  1,  2,  0,  2,  9,  9,  9,  2,  9,  1,  0,\n",
      "         9,  6,  0,  5,  1,  1,  3,  5,  9,  0,  6,  1,  3,  6,\n",
      "         0,  2,  2,  5,  0,  2,  2,  6,  9,  0,  2,  3,  5,  9,\n",
      "         0,  4,  9,  3])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "         2,  1,  5,  4])\n",
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      "         1,  3,  9,  0,  2,  9,  6,  0,  1,  9,  0,  0,  0,  0,\n",
      "         9,  6,  5,  6,  9,  6,  0,  2,  0,  4,  2,  4,  9,  0,\n",
      "         6,  1,  0,  6,  5,  0,  9,  2,  9,  0,  2,  6,  1,  2,\n",
      "         4,  9,  0,  0,  3,  9,  5,  1,  0,  9,  0,  9,  6,  6,\n",
      "         0,  3,  3,  5])\n",
      "\n",
      "Test set: Average loss: 0.0030, Accuracy: 7715/10000 (77%)\n",
      "\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-406af4775ab4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0mtrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m28\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m28\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m#torch.Size([200, 1, 28, 28])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m         \u001b[0mlogits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m#torch.Size([200, 10])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogits\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0;31m#print(loss.type())torch.FloatTensor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    489\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    490\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 491\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    492\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    493\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-9-ec5d9b7b3ccd>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    489\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    490\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 491\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    492\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    493\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     89\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_modules\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    489\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    490\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 491\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    492\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    493\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/linear.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mlinear\u001b[0;34m(input, weight, bias)\u001b[0m\n\u001b[1;32m    990\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mbias\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    991\u001b[0m         \u001b[0;31m# fused op is marginally faster\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 992\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddmm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    993\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    994\u001b[0m     \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model = MLP()\n",
    "optimizer = optim.Adam(model.parameters(),lr=learning_rate)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    for idx,(train,target) in enumerate(train_loader):\n",
    "        train = train.view(-1,28*28)#torch.Size([200, 1, 28, 28])\n",
    "        logits = model(train)#torch.Size([200, 10])\n",
    "        loss = criterion(logits,target)\n",
    "        #print(loss.type())torch.FloatTensor\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    \n",
    "#         if idx%100 == 0:\n",
    "#             print('Train epoch: {} [{}/{} ({:.0f}%)] \\t Loss: {:.6f}'\n",
    "#                       .format(epoch,idx*len(train),len(train_loader.dataset),100. * idx / len(train_loader),loss.item()))\n",
    "    test_loss = 0\n",
    "    correct = 0\n",
    "    for data,tar in test_loader:\n",
    "        data = data.view(-1,28*28)#\n",
    "        logs = model(data)\n",
    "        test_loss += criterion(logs,tar).item()\n",
    "        pre_data = logs.data.max(1)[1]#torch.Size([200, 10])\n",
    "        print(logs.data.max(1)[0])\n",
    "        print(logs.data.max(1)[1])\n",
    "        correct += pre_data.eq(tar).sum()\n",
    "    test_loss /= len(test_loader.dataset)\n",
    "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
    "        test_loss, correct, len(test_loader.dataset),\n",
    "        100. * correct / len(test_loader.dataset)))"
   ]
  },
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   },
   "outputs": [],
   "source": []
  }
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